Comments (2)
Thanks for the swift response!
Yes that's how I understood the generation as well. So yes, it is fair to assume that the named entities in these synthetic examples are unseen.
As stated in your paper:
In order to make ZeroRTE solvable in a supervised manner, we propose RelationPrompt to generate synthetic relation examples by prompting language models to generate structured texts.
So your suggestion is to solve ZeroRTE by generating data for these unseen labels. And since you train your Relation Extractor on these synthetic examples, it becomes a supervised setting.
I always understood Zero Shot as a setting where the model was not trained on any of the classes. But your solution is a fair approach π
from relationprompt.
Hi, thank you for your kind comments :)
We generate the synthetic samples based only on the label names of the unseen relations (eg "Military Rank", "Position Played" etc).
As we do not use any annotated triplets or sentences from the unseen relation data for training, this should still fulfill the zero-shot setting.
from relationprompt.
Related Issues (15)
- how to split single-triplet and multi-triplet HOT 1
- ζζ―ζδΈζη樑εε HOT 6
- data limits HOT 7
- Unseen Labels HOT 2
- where is the pretrain model of unseen=5 &unseen=15οΌ
- Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation. {'target': 250, 'success': 0, 'raw': 1}
- Creating own data splits HOT 1
- how to split data for relation classification ?
- question about Algorithm 1 on step 3 and 4. HOT 1
- connection timeout error is reported when using data to generate part of the code
- Hi! Iβm trying to run this code lately. I've got a question that why there is only pretrained model for unseen=10 seed=0.WHere should I find other modelsοΌ thank you HOT 6
- Demo - for line 5 and line 6 from algo HOT 1
- Zero shot Relation classification HOT 4
- Bug in Data Splitting on FewRel Dataset HOT 2
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from relationprompt.